diff --git a/src/test/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java b/src/test/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
index 76f4d96d8..6f349f2c1 100644
--- a/src/test/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
+++ b/src/test/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
@@ -17,6 +17,8 @@
 package org.apache.commons.math.stat.regression;
 
 import org.junit.Before;
+import org.junit.Test;
+import org.apache.commons.math.TestUtils;
 
 public class OLSMultipleLinearRegressionTest extends AbstractMultipleLinearRegressionTest {
 
@@ -49,5 +51,92 @@ public class OLSMultipleLinearRegressionTest extends AbstractMultipleLinearRegre
     protected int getSampleSize() {
         return y.length;
     }
-
+    
+    @Test
+    public void testPerfectFit() {
+        double[] betaHat = regression.estimateRegressionParameters();
+        TestUtils.assertEquals(betaHat, 
+          new double[]{11.0,0.5,0.666666666666667,0.75,0.8,0.8333333333333333},
+                1e-12);
+        double[] residuals = regression.estimateResiduals();
+        TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
+                      1e-12);
+        double[][] errors = regression.estimateRegressionParametersVariance();
+        // TODO: translate this into standard error vector and check
+    }
+    
+    
+    /**
+     * Test Longley dataset against certified values provided by NIST.
+     * Data Source: J. Longley (1967) "An Appraisal of Least Squares
+     * Programs for the Electronic Computer from the Point of View of the User"
+     * Journal of the American Statistical Association, vol. 62. September,
+     * pp. 819-841.
+     * 
+     * Certified values (and data) are from NIST:
+     * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
+     */
+    @Test
+    public void testLongly() {
+        // Y values are first, then independent vars
+        // Each row is one observation
+        double[] design = new double[] {
+            60323,83.0,234289,2356,1590,107608,1947,
+            61122,88.5,259426,2325,1456,108632,1948,
+            60171,88.2,258054,3682,1616,109773,1949,
+            61187,89.5,284599,3351,1650,110929,1950,
+            63221,96.2,328975,2099,3099,112075,1951,
+            63639,98.1,346999,1932,3594,113270,1952,
+            64989,99.0,365385,1870,3547,115094,1953,
+            63761,100.0,363112,3578,3350,116219,1954,
+            66019,101.2,397469,2904,3048,117388,1955,
+            67857,104.6,419180,2822,2857,118734,1956,
+            68169,108.4,442769,2936,2798,120445,1957,
+            66513,110.8,444546,4681,2637,121950,1958,
+            68655,112.6,482704,3813,2552,123366,1959,
+            69564,114.2,502601,3931,2514,125368,1960,
+            69331,115.7,518173,4806,2572,127852,1961,
+            70551,116.9,554894,4007,2827,130081,1962
+        };
+        
+        // Transform to Y and X required by interface
+        double[] y = new double[16];
+        double[][] x = new double[16][7];
+        int pointer = 0;
+        for (int i = 0; i < 16; i++) {
+            y[i] = design[pointer++];
+            x[i][0] = 1.0d;
+            for (int j = 1; j < 7; j++) {
+                x[i][j] = design[pointer++];
+            }
+        }
+        
+        // Estimate the model
+        MultipleLinearRegression model = new OLSMultipleLinearRegression();
+        model.addData(y, x, null);
+        
+        // Check expected beta values from NIST
+        double[] betaHat = model.estimateRegressionParameters();
+        TestUtils.assertEquals(betaHat, 
+          new double[]{-3482258.63459582, 15.0618722713733,
+                -0.358191792925910E-01,-2.02022980381683,
+                -1.03322686717359,-0.511041056535807E-01,
+                 1829.15146461355}, 1E-1); // <- UGH! need better accuracy!
+        
+        // Check expected residuals from R
+        double[] residuals = model.estimateResiduals();
+        TestUtils.assertEquals(residuals, new double[]{
+                267.340029759711,-94.0139423988359,46.28716775752924,
+                -410.114621930906,309.7145907602313,-249.3112153297231,
+                -164.0489563956039,-13.18035686637081,14.30477260005235,
+                 455.394094551857,-17.26892711483297,-39.0550425226967,
+                -155.5499735953195,-85.6713080421283,341.9315139607727,
+                -206.7578251937366},
+                      1E-2); // <- UGH again! need better accuracy!
+        
+        // Check standard errors from NIST
+        double[][] errors = model.estimateRegressionParametersVariance();
+        //TODO:  translate this into std error vector and check
+        
+    }
 }